Contribution of the in-vehicle microenvironment to individual ambient-source nitrogen dioxide exposure: the Multi-Ethnic Study of Atherosclerosis and Air Pollution

Measuring environmental exposures in people's activity space: The need to account for travel modes and exposure decay

BACKGROUND

Accurately quantifying people's out-of-home environmental exposure is important for identifying disease risk factors. Several activity space-based exposure assessments exist, possibly leading to different exposure estimates, and have neither considered individual travel modes nor exposure-related distance decay effects.

OBJECTIVE

We aimed (1) to develop an activity space-based exposure assessment approach that included travel modes and exposure-related distance decay effects and (2) to compare the size of such spaces and the exposure estimates derived from them across typically used activity space operationalizations.

METHODS

We used 7-day-long global positioning system (GPS)-enabled smartphone-based tracking data of 269 Dutch adults. People's GPS trajectory points were classified into passive and active travel modes. Exposure-related distance decay effects were modeled through linear, exponential, and Gaussian decay functions. We performed cross-comparisons on these three functional decay models and an unweighted model in conjunction with four activity space models (i.e., home-based buffers, minimum convex polygons, two standard deviational ellipses, and time-weighted GPS-based buffers). We applied non-parametric Kruskal-Wallis tests, pair-wise Wilcoxon signed-rank tests, and Spearman correlations to assess mean differences in the extent of the activity spaces and correlations across exposures to particulate matter (PM2.5), noise, green space, and blue space.

RESULTS

Participants spent, on average, 42% of their daily life out-of-home. We observed that including travel modes into activity space delineation resulted in significantly more compact activity spaces. Exposure estimates for PM2.5 and blue space were significantly (p < 0.05) different between exposure estimates that did or did not account for travel modes, unlike noise and green space, for which differences did not reach significance. While the inclusion of distance decay effects significantly affected noise and green space exposure assessments, the decay functions applied appear not to have had any impact on the results. We found that residential exposure estimates appear appropriate for use as proxy values for the overall amount of PM2.5 exposure in people's daily lives, while GPS-based assessments are suitable for noise, green space, and blue space.

SIGNIFICANCE

For some exposures, the tested activity space definitions, although significantly correlated, exhibited differing exposure estimate results based on inclusion or exclusion of travel modes or distance decay effect. Results only supported using home-based buffer values as proxies for individuals' daily short-term PM2.5 exposure.

Estimating exposure to traffic-related PM2.5 for women commuters using vehicle and personal monitoring

Exposure to traffic-related fine particulate matter air pollution (tr-PM_2.5) has been associated with adverse health outcomes including preterm birth and low birthweight. In-vehicle exposure to tr-PM_2.5 can contribute substantially to total tr-PM_2.5 exposure. Because average commuting habits of women differ from men, a research gap is estimating in-vehicle tr-PM_2.5 exposures for women commuters. For 46 women commuters in the Washington, D.C. metro area, we measured personal exposure to PM_2.5 during all vehicle trips taken in a 48-h sampling period. We also measured 48-h integrated PM_2.5 chemical constituents including black carbon and zinc. We identified trip times using vehicle monitors, specifically on-board diagnostics data loggers and dashboard cameras. For 386 trips, we estimated associations between PM_2.5 exposure and trip characteristics using linear mixed models accounting for participant, day, and time of day. Additionally, we estimated associations between rush hour trip PM_2.5 and 48-h integrated PM_2.5 chemical constituents using linear models. Exposure to PM_2.5 during trips was 1.9 μg/m³ (95% confidence interval (CI): 0.9, 2.9) higher than non-trip exposures and rush hour trip exposures were 3.2 μg/m³ (95% CI: 1.8, 4.6) higher than non-trip exposures on average. We did not find differences in PM_2.5 exposure by trip length. Although concentrations of tr-PM_2.5 chemical constituents were generally positively associated with rush hour trip PM_2.5, associations were weak indicating that other settings contribute to total tr-PM_2.5 exposure. Our study demonstrates the utility of combining vehicle monitors and personal PM_2.5 monitors for estimating personal exposure to tr-PM_2.5. Future work will investigate whether additional data collected by vehicle monitors, such as traffic and speed, can be leveraged to better understand tr-PM_2.5 exposure among commuters.

Integrating Modes of Transport in a Dynamic Modelling Approach to Evaluate Population Exposure to Ambient NO2 and PM2.5 Pollution in Urban Areas

To evaluate the effectiveness of alternative policies and measures to reduce air pollution effects on urban citizen's health, population exposure assessments are needed. Due to road traffic emissions being a major source of emissions and exposure in European cities, it is necessary to account for differentiated transport environments in population dynamics for exposure studies. In this study, we applied a modelling system to evaluate population exposure in the urban area of Hamburg in 2016. The modeling system consists of an urban-scale chemistry transport model to account for ambient air pollutant concentrations and a dynamic time-microenvironment-activity (TMA) approach, which accounts for population dynamics in different environments as well as for infiltration of outdoor to indoor air pollution. We integrated different modes of transport in the TMA approach to improve population exposure assessments in transport environments. The newly developed approach reports 12% more total exposure to NO₂ and 19% more to PM_2.5 compared with exposure estimates based on residential addresses. During the time people spend in different transport environments, the in-car environment contributes with 40% and 33% to the annual sum of exposure to NO₂ and PM_2.5, in the walking environment with 26% and 30%, in the cycling environment with 15% and 17% and other environments (buses, subway, suburban, and regional trains) with less than 10% respectively. The relative contribution of road traffic emissions to population exposure is highest in the in-car environment (57% for NO₂ and 15% for PM_2.5). Results for population-weighted exposure revealed exposure to PM_2.5 concentrations above the WHO AQG limit value in the cycling environment. Uncertainties for the exposure contributions arising from emissions and infiltration from outdoor to indoor pollutant concentrations range from -12% to +7% for NO₂ and PM_2.5. The developed "dynamic transport approach" is integrated in a computationally efficient exposure model, which is generally applicable in European urban areas. The presented methodology is promoted for use in urban mobility planning, e.g., to investigate on policy-driven changes in modal split and their combined effect on emissions, population activity and population exposure.

In-Vehicle Exposures at Transportation and the Health Concerns

In-vehicle environment is a special indoor environment, which is mobile, either open or closed. This chapter reviewed in-vehicle air quality and passenger exposures for roadway commuters, commercial airplanes, and marine transportation. The sources of pollutants in-vehicle can be categorized as the same as other indoor environments, including outdoor air, human activity, emission from building material and interior furnisher, and biological metabolic process from animals and microbes. However, the exposure in vehicles varies from now and then, influenced by window open/closed, speed, air flow, ventilation on/off, air conditioner on/off, pollutants from ambient outdoor air, interior material, and number of passengers. There are few studies on health condition of passengers, except infectious disease during airway transportation. Some health studies of related occupations are reviewed.

Heterostructural CuO-ZnO Nanocomposites: A Highly Selective Chemical and Electrochemical NO2 Sensor

A simple one-step chemical method is employed for the successful synthesis of CuO(50%)-ZnO(50%) nanocomposites (NCs) and investigation of their gas sensing properties. The X-ray diffraction studies revealed that these CuO-ZnO NCs display a hexagonal wurtzite-type crystal structure. The average width of 50-100 nm and length of 200-600 nm of the NCs were confirmed by transmission electron microscopic images, and the 1:1 proportion of Cu and Zn composition was confirmed by energy-dispersive spectra, i.e., CuO(50%)-ZnO(50%) NC studies. The CuO(50%)-ZnO(50%) NCs exhibit superior gas sensing performance with outstanding selectivity toward NO2 gas at a working temperature of 200 °C. Moreover, these NCs were used for the indirect evaluation of NO2 via electrochemical detection of NO2 - (as NO2 converts into NO2 - once it reacts with moisture, resulting into acid rain, i.e., indirect evaluation of NO2). As compared with other known modified electrodes, CuO(50%)-ZnO(50%) NCs show an apparent oxidation of NO2 - with a larger peak current for a wider linear range of nitrite concentration from 20 to 100 mM. We thus demonstrate that the as-synthesized CuO(50%)-ZnO(50%) NCs act as a promising low-cost NO2 sensor and further confirm their potential toward tunable gas sensors (electrochemical and solid state) (Scheme 1).

Exposures to Air Pollution and Noise from Multi-Modal Commuting in a Chinese City

Background: Modern urban travel includes mixtures of transit options, which potentially impact individual pollution exposures and health. This study aims to investigate variations in traffic-related air pollution and noise levels experienced in traffic in Chengdu, China. Methods: Real-time PM_2.5, black carbon (BC), and noise levels were measured for four transportation modes (car, bus, subway, and shared bike) on scripted routes in three types of neighborhoods (urban core, developing neighborhood, and suburb). Each mode of transportation in each neighborhood was sampled five times in summer and winter, respectively. After quality control, mixed effect models were built for the three pollutants separately. Results: Air pollutants had much higher concentrations in winter. Urban Core had the highest PM_2.5 and BC concentrations across seasons compared to the other neighborhoods. The mixed effect model indicated that car commutes were associated with lower PM_2.5 (−34.4 μg/m³; 95% CI: −47.5, −21.3), BC (−2016.4 ng/m³; 95% CI: −3383.8, −648.6), and noise (−9.3 dBA; 95% CI: −10.5, −8.0) levels compared with other modes; subway commutes had lower PM_2.5 (−11.9 μg/m³; 95% CI: 47.5, −21.3), but higher BC (2349.6 ng/m³; 95% CI: 978.1, 3722.1) and noise (3.0 dBA; 95% CI: 1.7, 4.3) levels than the other three modes of transportation. Conclusion: Personal exposure to air pollution and noise vary by season, neighborhood, and transportation modes. Exposure models accounting for environmental, meteorological, and behavioral factors, and duration of mixed mode commuting may be useful for health studies of urban traffic microenvironments.